RANPilot: Making AI Functionalities Robust to Dynamic O-RAN Reconfigurations
For O-RAN operators, this enables robust AI services despite dynamic network reconfigurations, solving a critical fragility that undermines O-RAN's flexibility.
RANPilot addresses AI performance degradation after O-RAN reconfigurations by proactively adapting models using a lightweight emulator to synthesize post-reconfiguration data, reducing AI downtime by 85-94% compared to reactive retraining.
The Open Radio Access Network (O-RAN) promises unprecedented flexibility through its reconfigurable architecture and AI-driven control. However, this agility exposes a critical fragility: AI models trained on one network configuration suffer significant performance degradation after an upgrade due to dramatic data drift. The standard solution, reactive retraining, is unacceptably slow, leaving the network in a suboptimal state for tens of minutes and undermining the core benefits of O-RAN's dynamism. This paper introduces RANPilot, the first framework to address this challenge through proactive AI adaptation. RANPilot constructs a lightweight "virtual O-RAN" (a trace-driven emulator) to synthesize high-fidelity training data representing the post-reconfiguration state before the physical change occurs, allowing AI models to be adapted in advance. Extensive experiments on a real-world 5G testbed demonstrate that RANPilot achieves near interruption-free AI services upon reconfiguration, reducing AI downtime by 85% to 94% against reactive baselines. By shifting the AI evolution paradigm from reactive redevelopment to proactive preparation, RANPilot explores a digital-leadoff approach to enable robust AI in reconfigurable O-RAN deployments.